Increasing amounts of memory for a computer have placed larger demands on computer processing. One field in which large amounts of data must be processed is health monitoring and diagnosis. Individuals, companies, and insurance companies desire to collect and analyze health data for as many individuals as possible to identify possible health risks and trends. For example, a company may collect and analyze health data for thousands of employees, each with diagnostic data related to dozens of diseases, prescribed medications, and health indicators (e.g., blood pressure, age, weight). The data can be stored in relational databases with the corresponding medical diagnostic codes. As the amount of health data increases, the ability to rapidly analyze it has suffered.
One tool that has been developed for managing a health and wellness program U.S. Patent Application Publication No. 2001/0039503 (“the '503 publication”). The '503 publication discloses collecting patient data from a variety of sources and filtering the patient data to discard some of the inputs that are not related to a particular illness that is being analyzed. The '503 publication analyzes the filtered data to provide recommendations to individuals based on identified health risks.
Although the tool of the '503 publication filters data that is unrelated to an analyzed illness, eliminating data can result in inaccurate predictions. For example, if the data is being analyzed for multiple diseases, eliminating data that is unrelated to a first disease may cause the '503 publication to improperly diagnose the patient for the second disease. Instead, summarizing, not eliminating, data would preserve data for diagnosis of multiple diseases. Moreover, where diagnosis of a particular disease involves analyzing hundreds of data points, the '503 publication fails to provide a method to reduce the data points in a manner that allows rapid processing without loss of accuracy.
The present disclosure is directed to overcoming one or more of the problems set forth above.